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robust-regression algorithm (iteratively reweighted least squares)  (MathWorks Inc)


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    MathWorks Inc robust-regression algorithm (iteratively reweighted least squares)
    Robust Regression Algorithm (Iteratively Reweighted Least Squares), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/robust+regression+algorithm/pm40355040-183-23-34
    Average 90 stars, based on 1 article reviews
    robust-regression algorithm (iteratively reweighted least squares) - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Cholinergic activity reflects reward expectations and predicts behavioral responses
    Article Snippet: Correlation between model parameters and anticipatory lick rate difference was calculated using the built-in robust regression algorithm of Matlab.

    Article Title: Investigation of cutting conditions on tool life in shoulder milling of Ti6Al4V using PVD coated micro-grain carbide insert based on design of experiments
    Article Snippet: The selected robust regression algorithm uses iteratively reweighted least squares with a bisquare weighting function ( https://se.mathworks.com ).

    Article Title: Investigation of the sensitivity of functional near-infrared spectroscopy brain imaging to anatomical variations in 5- to 11-year-old children
    Article Snippet: The DPF and PPF were computed for each 10-5 position by fitting the ∼ 120 measurements between 10 and 40 mm around each source to a linear regression model using an iterative robust regression algorithm (MATLAB function “robustfit”).

    Article Title: Improving microbial fitness in the mammalian gut by in vivo temporal functional metagenomics.
    Article Snippet: To estimate the relevant null a 2015 The Authors Molecular Systems Biology 11: 788 | 2015 D ow nloaded from https://w w w .em bopress.org on D ecem ber 11, 2024 from IP 112.204.230.112. hypotheses for the t-tests, while taking into account possible biases due to differential representation of genes in the input library, we used a robust regression algorithm (MATLAB function robustfit) in which the input library value served as the independent variable and the TA-RA or TA-NEC value served as the dependent variable.



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